Paper A v4.1: BCD-baseline reframe + screening positioning + trim

- Re-anchor inter-CPA coincidence-rate (ICCR) calibration on a normative
  non-Firm-A baseline (Firms B/C/D); Firm A held out as an out-of-sample
  target. Locked canonical numbers (codex-audited; Scripts 46/52/53):
  per-comparison HC 0.00014->0.000018, per-signature HC 0.0116, per-document
  HC+MC 0.34->0.1905; KDE crossover 0.837 retained corpus-wide.
- Reposition as an operator-tunable, semi-automated screening/triage framework
  (title -> "Automated Screening..."): HC = high-specificity operating point;
  MC band demoted to low-specificity advisory; Firm A = demonstration that the
  screening surfaces a templated end, audit-quality implications deferred.
- Apply codex prose-review fixes: triage-neutral five-way labels, soften
  mechanism/specificity wording, supersede MC claim-strength, update stale
  Appendix script references (40b/43/45 -> 46/52/53).
- Trim pass: compress Sec. V discussion + Sec. III echoes (27.7k -> 26.8k
  words); no substantive content removed.
- Add analysis scripts 45-53 (firm-year trends; BCD-only ICCR recompute;
  canonical-sampler locked numbers; Firm-A out-of-sample; BCD regression +
  cross-firm hit matrix).

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

signature_analysis/45_firm_year_trends.py

@@ -0,0 +1,60 @@
+#!/usr/bin/env python3
+"""Firm x year descriptor trends (B-gate diagnostic).
+
+Plots per-firm yearly mean cosine, mean dHash, and HC-box hit share to test
+whether Firms B/C/D show a 2020 structural break converging toward Firm A.
+Read-only against the production DB.
+"""
+import sqlite3
+import matplotlib.pyplot as plt
+
+DB = '/Volumes/NV2/PDF-Processing/signature-analysis/signature_analysis.db'
+FIRMS = [('勤業眾信聯合', 'Firm A (Deloitte)', '#d62728'),
+         ('安侯建業聯合', 'Firm B (KPMG)', '#1f77b4'),
+         ('資誠聯合', 'Firm C (PwC)', '#2ca02c'),
+         ('安永聯合', 'Firm D (EY)', '#ff7f0e')]
+
+conn = sqlite3.connect(f'file:{DB}?mode=ro', uri=True)
+cur = conn.cursor()
+
+
+def series(firm_zh):
+    cur.execute("""
+        SELECT CAST(substr(s.year_month,1,4) AS INT) AS yr,
+               AVG(s.max_similarity_to_same_accountant),
+               AVG(s.min_dhash_independent),
+               AVG(CASE WHEN s.max_similarity_to_same_accountant>0.95
+                         AND s.min_dhash_independent<=5 THEN 1.0 ELSE 0.0 END),
+               COUNT(*)
+        FROM signatures s JOIN accountants a ON s.assigned_accountant=a.name
+        WHERE a.firm=? AND s.year_month IS NOT NULL
+          AND s.max_similarity_to_same_accountant IS NOT NULL
+          AND s.min_dhash_independent IS NOT NULL
+        GROUP BY yr ORDER BY yr""", (firm_zh,))
+    return cur.fetchall()
+
+
+fig, axes = plt.subplots(1, 3, figsize=(16, 4.8))
+for firm_zh, label, color in FIRMS:
+    rows = series(firm_zh)
+    yrs = [r[0] for r in rows]
+    axes[0].plot(yrs, [r[1] for r in rows], 'o-', color=color, label=label)
+    axes[1].plot(yrs, [r[2] for r in rows], 'o-', color=color, label=label)
+    axes[2].plot(yrs, [r[3] for r in rows], 'o-', color=color, label=label)
+
+for ax in axes:
+    ax.axvline(2020, ls='--', color='grey', alpha=0.6)
+    ax.text(2020.05, ax.get_ylim()[0], ' 2020', color='grey', fontsize=8, va='bottom')
+    ax.set_xlabel('Fiscal year')
+    ax.grid(alpha=0.3)
+axes[0].set_title('Mean best-match cosine'); axes[0].axhline(0.95, ls=':', color='k', alpha=0.4)
+axes[1].set_title('Mean independent-min dHash'); axes[1].axhline(5, ls=':', color='k', alpha=0.4)
+axes[2].set_title('HC-box share (cos>0.95 & dHash$\\leq$5)')
+axes[0].legend(fontsize=8, loc='lower right')
+fig.suptitle('Big-4 descriptor trends 2013–2023 (2023 = partial, to Apr)  —  no 2020 break, no convergence to A',
+             fontsize=11)
+fig.tight_layout()
+out = '/Volumes/NV2/pdf_recognize/signature_analysis/firm_year_trends.png'
+fig.savefig(out, dpi=130, bbox_inches='tight')
+print('saved', out)
+conn.close()